# PCNNA: A Photonic Convolutional Neural Network Accelerator

arXiv: https://arxiv.org/abs/1807.08792 · HTML: https://mlsanity.com/paper/1807.08792/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning](https://mlsanity.com/paper/2102.10140/) (2021)
- [Mirage: An RNS-Based Photonic Accelerator for DNN Training](https://mlsanity.com/paper/2311.17323/) (2023)
- [PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator](https://mlsanity.com/paper/2211.05276/) (2022)
- [PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics](https://mlsanity.com/paper/2501.13828/) (2025)
- [LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)](https://mlsanity.com/paper/2206.13861/) (2022)
- [Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection](https://mlsanity.com/paper/2309.16783/) (2023)
- [A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators](https://mlsanity.com/paper/2402.03149/) (2024)
- [Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics](https://mlsanity.com/paper/2603.07626/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNN) have been the centerpiece of many applications including but not limited to computer vision, speech processing, and Natural Language Processing (NLP). However, the computationally expensive convolution operations impose many challenges to the performance and scalability of CNNs. In parallel, photonic systems, which are traditionally employed for data communication, have enjoyed recent popularity for data processing due to their high bandwidth, low power consumption, and reconfigurability. Here we propose a Photonic Convolutional Neural Network Accelerator (PCNNA) as a proof of concept design to speedup the convolution operation for CNNs. Our design is based on the recently introduced silicon photonic microring weight banks, which use broadcast-and-weight protocol to perform Multiply And Accumulate (MAC) operation and move data through layers of a neural network. Here, we aim to exploit the synergy between the inherent parallelism of photonics in the form of Wavelength Division Multiplexing (WDM) and sparsity of connections between input feature maps and kernels in CNNs. While our full system design offers up to more than 3 orders of magnitude speedup in execution time, its optical core potentially offers more than 5 order of magnitude speedup compared to state-of-the-art electronic counterparts.